Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations300
Missing cells1605
Missing cells (%)19.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.8 KiB
Average record size in memory224.4 B

Variable types

Categorical21
Numeric7

Alerts

V1 is highly overall correlated with V24High correlation
V10 is highly overall correlated with V27High correlation
V11 is highly overall correlated with V27High correlation
V13 is highly overall correlated with V27High correlation
V15 is highly overall correlated with V27High correlation
V16 is highly overall correlated with V2 and 4 other fieldsHigh correlation
V18 is highly overall correlated with V24 and 1 other fieldsHigh correlation
V2 is highly overall correlated with V16 and 2 other fieldsHigh correlation
V22 is highly overall correlated with V16 and 2 other fieldsHigh correlation
V24 is highly overall correlated with V1 and 1 other fieldsHigh correlation
V26 is highly overall correlated with V16High correlation
V27 is highly overall correlated with V10 and 9 other fieldsHigh correlation
V28 is highly overall correlated with V16 and 1 other fieldsHigh correlation
V3 is highly overall correlated with V2 and 1 other fieldsHigh correlation
V5 is highly overall correlated with V2High correlation
V6 is highly overall correlated with V27High correlation
V7 is highly overall correlated with V27High correlation
V8 is highly overall correlated with V27High correlation
V9 is highly overall correlated with V27High correlation
V2 is highly imbalanced (59.8%) Imbalance
V27 is highly imbalanced (96.8%) Imbalance
V4 has 60 (20.0%) missing values Missing
V5 has 24 (8.0%) missing values Missing
V6 has 58 (19.3%) missing values Missing
V7 has 56 (18.7%) missing values Missing
V8 has 69 (23.0%) missing values Missing
V9 has 47 (15.7%) missing values Missing
V10 has 32 (10.7%) missing values Missing
V11 has 55 (18.3%) missing values Missing
V12 has 44 (14.7%) missing values Missing
V13 has 56 (18.7%) missing values Missing
V14 has 104 (34.7%) missing values Missing
V15 has 106 (35.3%) missing values Missing
V16 has 247 (82.3%) missing values Missing
V17 has 102 (34.0%) missing values Missing
V18 has 118 (39.3%) missing values Missing
V19 has 29 (9.7%) missing values Missing
V20 has 33 (11.0%) missing values Missing
V21 has 165 (55.0%) missing values Missing
V22 has 198 (66.0%) missing values Missing
V25 has 56 (18.7%) zeros Zeros
V26 has 293 (97.7%) zeros Zeros

Reproduction

Analysis started2025-07-01 20:45:51.694357
Analysis finished2025-07-01 20:46:07.848784
Duration16.15 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

V1
Categorical

High correlation 

Distinct2
Distinct (%)0.7%
Missing1
Missing (%)0.3%
Memory size2.5 KiB
1
180 
2
119 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters299
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 180
60.0%
2 119
39.7%
(Missing) 1
 
0.3%

Length

2025-07-01T22:46:08.037291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:08.103235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 180
60.2%
2 119
39.8%

Most occurring characters

ValueCountFrequency (%)
1 180
60.2%
2 119
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 180
60.2%
2 119
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 180
60.2%
2 119
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 180
60.2%
2 119
39.8%

V2
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
1
276 
9
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row9
5th row1

Common Values

ValueCountFrequency (%)
1 276
92.0%
9 24
 
8.0%

Length

2025-07-01T22:46:08.171976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:08.221480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 276
92.0%
9 24
 
8.0%

Most occurring characters

ValueCountFrequency (%)
1 276
92.0%
9 24
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 276
92.0%
9 24
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 276
92.0%
9 24
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 276
92.0%
9 24
 
8.0%

V3
Real number (ℝ)

High correlation 

Distinct284
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1085888.8
Minimum518476
Maximum5305629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-01T22:46:08.303945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum518476
5-th percentile527696.95
Q1528904
median530305.5
Q3534727.5
95-th percentile5290787.5
Maximum5305629
Range4787153
Interquartile range (IQR)5823.5

Descriptive statistics

Standard deviation1529800.9
Coefficient of variation (CV)1.4088007
Kurtosis3.7863713
Mean1085888.8
Median Absolute Deviation (MAD)2377.5
Skewness2.4002271
Sum3.2576665 × 108
Variance2.3402908 × 1012
MonotonicityNot monotonic
2025-07-01T22:46:08.403849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
528729 2
 
0.7%
532349 2
 
0.7%
527544 2
 
0.7%
529424 2
 
0.7%
529461 2
 
0.7%
530693 2
 
0.7%
528469 2
 
0.7%
5279822 2
 
0.7%
529796 2
 
0.7%
528931 2
 
0.7%
Other values (274) 280
93.3%
ValueCountFrequency (%)
518476 1
0.3%
521399 1
0.3%
521681 1
0.3%
522979 1
0.3%
523190 1
0.3%
526639 1
0.3%
526802 1
0.3%
527365 1
0.3%
527463 1
0.3%
527518 1
0.3%
ValueCountFrequency (%)
5305629 1
0.3%
5305129 1
0.3%
5301219 1
0.3%
5299629 1
0.3%
5299603 1
0.3%
5299253 1
0.3%
5297379 1
0.3%
5297159 1
0.3%
5294539 1
0.3%
5294369 1
0.3%

V4
Real number (ℝ)

Missing 

Distinct40
Distinct (%)16.7%
Missing60
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean38.167917
Minimum35.4
Maximum40.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-01T22:46:08.503395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35.4
5-th percentile37.1
Q137.8
median38.2
Q338.5
95-th percentile39.4
Maximum40.8
Range5.4
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.73228866
Coefficient of variation (CV)0.019185974
Kurtosis1.6815821
Mean38.167917
Median Absolute Deviation (MAD)0.4
Skewness0.033551141
Sum9160.3
Variance0.53624669
MonotonicityNot monotonic
2025-07-01T22:46:08.618977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
38 26
 
8.7%
38.5 19
 
6.3%
38.3 18
 
6.0%
37.8 17
 
5.7%
38.2 16
 
5.3%
37.5 12
 
4.0%
38.1 12
 
4.0%
38.6 12
 
4.0%
38.4 11
 
3.7%
38.7 7
 
2.3%
Other values (30) 90
30.0%
(Missing) 60
20.0%
ValueCountFrequency (%)
35.4 1
 
0.3%
36 1
 
0.3%
36.1 1
 
0.3%
36.4 1
 
0.3%
36.5 2
0.7%
36.6 1
 
0.3%
36.8 1
 
0.3%
36.9 1
 
0.3%
37 2
0.7%
37.1 3
1.0%
ValueCountFrequency (%)
40.8 1
 
0.3%
40.3 2
 
0.7%
40 1
 
0.3%
39.9 1
 
0.3%
39.7 1
 
0.3%
39.6 1
 
0.3%
39.5 4
1.3%
39.4 3
1.0%
39.3 4
1.3%
39.2 5
1.7%

V5
Real number (ℝ)

High correlation  Missing 

Distinct52
Distinct (%)18.8%
Missing24
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean71.913043
Minimum30
Maximum184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-01T22:46:08.729239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile40
Q148
median64
Q388
95-th percentile125
Maximum184
Range154
Interquartile range (IQR)40

Descriptive statistics

Standard deviation28.630557
Coefficient of variation (CV)0.39812745
Kurtosis0.72380218
Mean71.913043
Median Absolute Deviation (MAD)18
Skewness1.0230835
Sum19848
Variance819.70877
MonotonicityNot monotonic
2025-07-01T22:46:08.836529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 28
 
9.3%
60 25
 
8.3%
40 18
 
6.0%
88 12
 
4.0%
52 12
 
4.0%
100 11
 
3.7%
44 11
 
3.7%
72 11
 
3.7%
42 10
 
3.3%
120 10
 
3.3%
Other values (42) 128
42.7%
(Missing) 24
 
8.0%
ValueCountFrequency (%)
30 2
 
0.7%
36 3
 
1.0%
38 1
 
0.3%
40 18
6.0%
42 10
 
3.3%
44 11
 
3.7%
45 2
 
0.7%
46 1
 
0.3%
48 28
9.3%
49 1
 
0.3%
ValueCountFrequency (%)
184 1
0.3%
164 1
0.3%
160 1
0.3%
150 2
0.7%
146 1
0.3%
140 2
0.7%
136 1
0.3%
132 1
0.3%
130 2
0.7%
129 1
0.3%

V6
Categorical

High correlation  Missing 

Distinct40
Distinct (%)16.5%
Missing58
Missing (%)19.3%
Memory size2.5 KiB
20
28 
24
27 
16
22 
12
19 
30
19 
Other values (35)
127 

Length

Max length2
Median length2
Mean length1.9876033
Min length1

Characters and Unicode

Total characters481
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)5.0%

Sample

1st row28
2nd row20
3rd row24
4th row84
5th row35

Common Values

ValueCountFrequency (%)
20 28
9.3%
24 27
9.0%
16 22
 
7.3%
12 19
 
6.3%
30 19
 
6.3%
40 17
 
5.7%
36 16
 
5.3%
28 13
 
4.3%
32 11
 
3.7%
18 8
 
2.7%
Other values (30) 62
20.7%
(Missing) 58
19.3%

Length

2025-07-01T22:46:08.936214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20 28
11.6%
24 27
11.2%
16 22
 
9.1%
12 19
 
7.9%
30 19
 
7.9%
40 17
 
7.0%
36 16
 
6.6%
28 13
 
5.4%
32 11
 
4.5%
18 8
 
3.3%
Other values (30) 62
25.6%

Most occurring characters

ValueCountFrequency (%)
2 112
23.3%
0 80
16.6%
4 66
13.7%
1 62
12.9%
3 52
10.8%
6 50
10.4%
8 39
 
8.1%
5 11
 
2.3%
9 6
 
1.2%
7 3
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 481
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 112
23.3%
0 80
16.6%
4 66
13.7%
1 62
12.9%
3 52
10.8%
6 50
10.4%
8 39
 
8.1%
5 11
 
2.3%
9 6
 
1.2%
7 3
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 481
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 112
23.3%
0 80
16.6%
4 66
13.7%
1 62
12.9%
3 52
10.8%
6 50
10.4%
8 39
 
8.1%
5 11
 
2.3%
9 6
 
1.2%
7 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 481
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 112
23.3%
0 80
16.6%
4 66
13.7%
1 62
12.9%
3 52
10.8%
6 50
10.4%
8 39
 
8.1%
5 11
 
2.3%
9 6
 
1.2%
7 3
 
0.6%

V7
Categorical

High correlation  Missing 

Distinct4
Distinct (%)1.6%
Missing56
Missing (%)18.7%
Memory size2.5 KiB
3
109 
1
78 
2
30 
4
27 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters244
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row4
4th row2
5th row1

Common Values

ValueCountFrequency (%)
3 109
36.3%
1 78
26.0%
2 30
 
10.0%
4 27
 
9.0%
(Missing) 56
18.7%

Length

2025-07-01T22:46:09.004579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:09.071709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 109
44.7%
1 78
32.0%
2 30
 
12.3%
4 27
 
11.1%

Most occurring characters

ValueCountFrequency (%)
3 109
44.7%
1 78
32.0%
2 30
 
12.3%
4 27
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 244
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 109
44.7%
1 78
32.0%
2 30
 
12.3%
4 27
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 244
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 109
44.7%
1 78
32.0%
2 30
 
12.3%
4 27
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 244
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 109
44.7%
1 78
32.0%
2 30
 
12.3%
4 27
 
11.1%

V8
Categorical

High correlation  Missing 

Distinct4
Distinct (%)1.7%
Missing69
Missing (%)23.0%
Memory size2.5 KiB
1
115 
3
103 
4
 
8
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters231
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 115
38.3%
3 103
34.3%
4 8
 
2.7%
2 5
 
1.7%
(Missing) 69
23.0%

Length

2025-07-01T22:46:09.144934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:09.197073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 115
49.8%
3 103
44.6%
4 8
 
3.5%
2 5
 
2.2%

Most occurring characters

ValueCountFrequency (%)
1 115
49.8%
3 103
44.6%
4 8
 
3.5%
2 5
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 115
49.8%
3 103
44.6%
4 8
 
3.5%
2 5
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 115
49.8%
3 103
44.6%
4 8
 
3.5%
2 5
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 115
49.8%
3 103
44.6%
4 8
 
3.5%
2 5
 
2.2%

V9
Categorical

High correlation  Missing 

Distinct6
Distinct (%)2.4%
Missing47
Missing (%)15.7%
Memory size2.5 KiB
1
79 
3
58 
4
41 
2
30 
5
25 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters253
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row6
4th row6
5th row3

Common Values

ValueCountFrequency (%)
1 79
26.3%
3 58
19.3%
4 41
13.7%
2 30
 
10.0%
5 25
 
8.3%
6 20
 
6.7%
(Missing) 47
15.7%

Length

2025-07-01T22:46:09.265286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:09.328393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 79
31.2%
3 58
22.9%
4 41
16.2%
2 30
 
11.9%
5 25
 
9.9%
6 20
 
7.9%

Most occurring characters

ValueCountFrequency (%)
1 79
31.2%
3 58
22.9%
4 41
16.2%
2 30
 
11.9%
5 25
 
9.9%
6 20
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 253
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 79
31.2%
3 58
22.9%
4 41
16.2%
2 30
 
11.9%
5 25
 
9.9%
6 20
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 253
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 79
31.2%
3 58
22.9%
4 41
16.2%
2 30
 
11.9%
5 25
 
9.9%
6 20
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 253
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 79
31.2%
3 58
22.9%
4 41
16.2%
2 30
 
11.9%
5 25
 
9.9%
6 20
 
7.9%

V10
Categorical

High correlation  Missing 

Distinct3
Distinct (%)1.1%
Missing32
Missing (%)10.7%
Memory size2.5 KiB
1
188 
2
78 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters268
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 188
62.7%
2 78
26.0%
3 2
 
0.7%
(Missing) 32
 
10.7%

Length

2025-07-01T22:46:09.424347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:09.480649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 188
70.1%
2 78
29.1%
3 2
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 188
70.1%
2 78
29.1%
3 2
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 188
70.1%
2 78
29.1%
3 2
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 188
70.1%
2 78
29.1%
3 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 188
70.1%
2 78
29.1%
3 2
 
0.7%

V11
Categorical

High correlation  Missing 

Distinct5
Distinct (%)2.0%
Missing55
Missing (%)18.3%
Memory size2.5 KiB
3
67 
2
59 
5
42 
4
39 
1
38 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters245
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row3
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 67
22.3%
2 59
19.7%
5 42
14.0%
4 39
13.0%
1 38
12.7%
(Missing) 55
18.3%

Length

2025-07-01T22:46:09.548974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:09.617222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 67
27.3%
2 59
24.1%
5 42
17.1%
4 39
15.9%
1 38
15.5%

Most occurring characters

ValueCountFrequency (%)
3 67
27.3%
2 59
24.1%
5 42
17.1%
4 39
15.9%
1 38
15.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 245
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 67
27.3%
2 59
24.1%
5 42
17.1%
4 39
15.9%
1 38
15.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 245
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 67
27.3%
2 59
24.1%
5 42
17.1%
4 39
15.9%
1 38
15.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 245
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 67
27.3%
2 59
24.1%
5 42
17.1%
4 39
15.9%
1 38
15.5%

V12
Categorical

Missing 

Distinct4
Distinct (%)1.6%
Missing44
Missing (%)14.7%
Memory size2.5 KiB
3
128 
4
73 
1
39 
2
16 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters256
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 128
42.7%
4 73
24.3%
1 39
 
13.0%
2 16
 
5.3%
(Missing) 44
 
14.7%

Length

2025-07-01T22:46:09.697238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:09.753928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 128
50.0%
4 73
28.5%
1 39
 
15.2%
2 16
 
6.2%

Most occurring characters

ValueCountFrequency (%)
3 128
50.0%
4 73
28.5%
1 39
 
15.2%
2 16
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 128
50.0%
4 73
28.5%
1 39
 
15.2%
2 16
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 128
50.0%
4 73
28.5%
1 39
 
15.2%
2 16
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 128
50.0%
4 73
28.5%
1 39
 
15.2%
2 16
 
6.2%

V13
Categorical

High correlation  Missing 

Distinct4
Distinct (%)1.6%
Missing56
Missing (%)18.7%
Memory size2.5 KiB
1
76 
3
65 
2
65 
4
38 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters244
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row1
4th row4
5th row2

Common Values

ValueCountFrequency (%)
1 76
25.3%
3 65
21.7%
2 65
21.7%
4 38
12.7%
(Missing) 56
18.7%

Length

2025-07-01T22:46:09.827740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:09.878267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 76
31.1%
3 65
26.6%
2 65
26.6%
4 38
15.6%

Most occurring characters

ValueCountFrequency (%)
1 76
31.1%
3 65
26.6%
2 65
26.6%
4 38
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 244
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 76
31.1%
3 65
26.6%
2 65
26.6%
4 38
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 244
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 76
31.1%
3 65
26.6%
2 65
26.6%
4 38
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 244
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 76
31.1%
3 65
26.6%
2 65
26.6%
4 38
15.6%

V14
Categorical

Missing 

Distinct3
Distinct (%)1.5%
Missing104
Missing (%)34.7%
Memory size2.5 KiB
2
102 
1
71 
3
23 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters196
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 102
34.0%
1 71
23.7%
3 23
 
7.7%
(Missing) 104
34.7%

Length

2025-07-01T22:46:09.955829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:10.013263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 102
52.0%
1 71
36.2%
3 23
 
11.7%

Most occurring characters

ValueCountFrequency (%)
2 102
52.0%
1 71
36.2%
3 23
 
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 102
52.0%
1 71
36.2%
3 23
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 102
52.0%
1 71
36.2%
3 23
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 102
52.0%
1 71
36.2%
3 23
 
11.7%

V15
Categorical

High correlation  Missing 

Distinct3
Distinct (%)1.5%
Missing106
Missing (%)35.3%
Memory size2.5 KiB
1
120 
3
39 
2
35 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 120
40.0%
3 39
 
13.0%
2 35
 
11.7%
(Missing) 106
35.3%

Length

2025-07-01T22:46:10.079974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:10.130150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 120
61.9%
3 39
 
20.1%
2 35
 
18.0%

Most occurring characters

ValueCountFrequency (%)
1 120
61.9%
3 39
 
20.1%
2 35
 
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 194
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 120
61.9%
3 39
 
20.1%
2 35
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 194
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 120
61.9%
3 39
 
20.1%
2 35
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 194
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 120
61.9%
3 39
 
20.1%
2 35
 
18.0%

V16
Categorical

High correlation  Missing 

Distinct20
Distinct (%)37.7%
Missing247
Missing (%)82.3%
Memory size2.5 KiB
2
9 
7.00
8 
6.50
5 
5.00
4 
5.50
4 
Other values (15)
23 

Length

Max length4
Median length4
Mean length3.3773585
Min length1

Characters and Unicode

Total characters179
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)18.9%

Sample

1st row5.00
2nd row3.00
3rd row7.20
4th row4.50
5th row5.50

Common Values

ValueCountFrequency (%)
2 9
 
3.0%
7.00 8
 
2.7%
6.50 5
 
1.7%
5.00 4
 
1.3%
5.50 4
 
1.3%
4.00 3
 
1.0%
3.00 3
 
1.0%
4.50 3
 
1.0%
7.50 2
 
0.7%
1 2
 
0.7%
Other values (10) 10
 
3.3%
(Missing) 247
82.3%

Length

2025-07-01T22:46:10.205092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 9
17.0%
7.00 8
15.1%
6.50 5
9.4%
5.00 4
 
7.5%
5.50 4
 
7.5%
4.00 3
 
5.7%
3.00 3
 
5.7%
4.50 3
 
5.7%
7.50 2
 
3.8%
1 2
 
3.8%
Other values (10) 10
18.9%

Most occurring characters

ValueCountFrequency (%)
0 61
34.1%
. 42
23.5%
5 27
15.1%
7 12
 
6.7%
2 11
 
6.1%
4 10
 
5.6%
6 7
 
3.9%
3 6
 
3.4%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 179
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61
34.1%
. 42
23.5%
5 27
15.1%
7 12
 
6.7%
2 11
 
6.1%
4 10
 
5.6%
6 7
 
3.9%
3 6
 
3.4%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 179
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61
34.1%
. 42
23.5%
5 27
15.1%
7 12
 
6.7%
2 11
 
6.1%
4 10
 
5.6%
6 7
 
3.9%
3 6
 
3.4%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 179
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61
34.1%
. 42
23.5%
5 27
15.1%
7 12
 
6.7%
2 11
 
6.1%
4 10
 
5.6%
6 7
 
3.9%
3 6
 
3.4%
1 3
 
1.7%

V17
Categorical

Missing 

Distinct4
Distinct (%)2.0%
Missing102
Missing (%)34.0%
Memory size2.5 KiB
4
79 
1
57 
3
49 
2
13 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters198
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 79
26.3%
1 57
19.0%
3 49
16.3%
2 13
 
4.3%
(Missing) 102
34.0%

Length

2025-07-01T22:46:10.277537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:10.328532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 79
39.9%
1 57
28.8%
3 49
24.7%
2 13
 
6.6%

Most occurring characters

ValueCountFrequency (%)
4 79
39.9%
1 57
28.8%
3 49
24.7%
2 13
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 79
39.9%
1 57
28.8%
3 49
24.7%
2 13
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 79
39.9%
1 57
28.8%
3 49
24.7%
2 13
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 79
39.9%
1 57
28.8%
3 49
24.7%
2 13
 
6.6%

V18
Categorical

High correlation  Missing 

Distinct5
Distinct (%)2.7%
Missing118
Missing (%)39.3%
Memory size2.5 KiB
5
79 
4
43 
1
28 
2
19 
3
13 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row2
3rd row1
4th row3
5th row5

Common Values

ValueCountFrequency (%)
5 79
26.3%
4 43
 
14.3%
1 28
 
9.3%
2 19
 
6.3%
3 13
 
4.3%
(Missing) 118
39.3%

Length

2025-07-01T22:46:10.409045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:10.468580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 79
43.4%
4 43
23.6%
1 28
 
15.4%
2 19
 
10.4%
3 13
 
7.1%

Most occurring characters

ValueCountFrequency (%)
5 79
43.4%
4 43
23.6%
1 28
 
15.4%
2 19
 
10.4%
3 13
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 79
43.4%
4 43
23.6%
1 28
 
15.4%
2 19
 
10.4%
3 13
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 79
43.4%
4 43
23.6%
1 28
 
15.4%
2 19
 
10.4%
3 13
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 79
43.4%
4 43
23.6%
1 28
 
15.4%
2 19
 
10.4%
3 13
 
7.1%

V19
Real number (ℝ)

Missing 

Distinct50
Distinct (%)18.5%
Missing29
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean46.295203
Minimum23
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-01T22:46:10.559683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile33
Q138
median45
Q352
95-th percentile67.5
Maximum75
Range52
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.419335
Coefficient of variation (CV)0.22506294
Kurtosis0.094093098
Mean46.295203
Median Absolute Deviation (MAD)7
Skewness0.70013509
Sum12546
Variance108.56253
MonotonicityNot monotonic
2025-07-01T22:46:10.655869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 16
 
5.3%
50 16
 
5.3%
43 14
 
4.7%
45 14
 
4.7%
44 13
 
4.3%
40 12
 
4.0%
36 11
 
3.7%
47 10
 
3.3%
35 10
 
3.3%
46 9
 
3.0%
Other values (40) 146
48.7%
(Missing) 29
 
9.7%
ValueCountFrequency (%)
23 1
 
0.3%
24 1
 
0.3%
26 1
 
0.3%
28 1
 
0.3%
30 1
 
0.3%
31 2
 
0.7%
31.5 1
 
0.3%
32 2
 
0.7%
33 8
2.7%
34 5
1.7%
ValueCountFrequency (%)
75 2
0.7%
74 1
 
0.3%
73 2
0.7%
72 1
 
0.3%
71 1
 
0.3%
70 1
 
0.3%
69 2
0.7%
68 4
1.3%
67 1
 
0.3%
66 3
1.0%

V20
Real number (ℝ)

Missing 

Distinct81
Distinct (%)30.3%
Missing33
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean24.456929
Minimum3.3
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-01T22:46:10.753309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile5.5
Q16.5
median7.5
Q357
95-th percentile74.7
Maximum89
Range85.7
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation27.475009
Coefficient of variation (CV)1.1234039
Kurtosis-0.86087505
Mean24.456929
Median Absolute Deviation (MAD)1.2
Skewness0.99507202
Sum6530
Variance754.87615
MonotonicityNot monotonic
2025-07-01T22:46:10.860016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 15
 
5.0%
6.5 15
 
5.0%
7.5 13
 
4.3%
6.6 11
 
3.7%
7.2 9
 
3.0%
5.9 8
 
2.7%
6.7 8
 
2.7%
65 8
 
2.7%
6 7
 
2.3%
6.8 6
 
2.0%
Other values (71) 167
55.7%
(Missing) 33
 
11.0%
ValueCountFrequency (%)
3.3 1
 
0.3%
4 1
 
0.3%
4.5 2
0.7%
4.6 1
 
0.3%
4.7 1
 
0.3%
4.9 2
0.7%
5 1
 
0.3%
5.3 2
0.7%
5.5 4
1.3%
5.7 3
1.0%
ValueCountFrequency (%)
89 1
 
0.3%
86 1
 
0.3%
85 1
 
0.3%
82 1
 
0.3%
81 2
0.7%
80 2
0.7%
77 2
0.7%
76 1
 
0.3%
75 3
1.0%
74 2
0.7%

V21
Categorical

Missing 

Distinct3
Distinct (%)2.2%
Missing165
Missing (%)55.0%
Memory size2.5 KiB
2
48 
3
46 
1
41 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters135
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 48
 
16.0%
3 46
 
15.3%
1 41
 
13.7%
(Missing) 165
55.0%

Length

2025-07-01T22:46:10.955886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:11.012695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 48
35.6%
3 46
34.1%
1 41
30.4%

Most occurring characters

ValueCountFrequency (%)
2 48
35.6%
3 46
34.1%
1 41
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 48
35.6%
3 46
34.1%
1 41
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 48
35.6%
3 46
34.1%
1 41
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 48
35.6%
3 46
34.1%
1 41
30.4%

V22
Categorical

High correlation  Missing 

Distinct39
Distinct (%)38.2%
Missing198
Missing (%)66.0%
Memory size2.5 KiB
2
25 
1
15 
3.90
 
4
2.60
 
4
5.00
 
3
Other values (34)
51 

Length

Max length5
Median length4
Mean length2.8431373
Min length1

Characters and Unicode

Total characters290
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)20.6%

Sample

1st row2
2nd row5.30
3rd row2.20
4th row3.60
5th row2

Common Values

ValueCountFrequency (%)
2 25
 
8.3%
1 15
 
5.0%
3.90 4
 
1.3%
2.60 4
 
1.3%
5.00 3
 
1.0%
7.00 3
 
1.0%
3.60 3
 
1.0%
2.80 3
 
1.0%
3.40 3
 
1.0%
4.50 2
 
0.7%
Other values (29) 37
 
12.3%
(Missing) 198
66.0%

Length

2025-07-01T22:46:11.086652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 25
24.5%
1 15
14.7%
3.90 4
 
3.9%
2.60 4
 
3.9%
5.00 3
 
2.9%
7.00 3
 
2.9%
3.60 3
 
2.9%
2.80 3
 
2.9%
3.40 3
 
2.9%
4.50 2
 
2.0%
Other values (29) 37
36.3%

Most occurring characters

ValueCountFrequency (%)
0 80
27.6%
. 62
21.4%
2 42
14.5%
1 29
 
10.0%
3 23
 
7.9%
4 15
 
5.2%
6 13
 
4.5%
5 10
 
3.4%
7 6
 
2.1%
9 5
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 80
27.6%
. 62
21.4%
2 42
14.5%
1 29
 
10.0%
3 23
 
7.9%
4 15
 
5.2%
6 13
 
4.5%
5 10
 
3.4%
7 6
 
2.1%
9 5
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 80
27.6%
. 62
21.4%
2 42
14.5%
1 29
 
10.0%
3 23
 
7.9%
4 15
 
5.2%
6 13
 
4.5%
5 10
 
3.4%
7 6
 
2.1%
9 5
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 80
27.6%
. 62
21.4%
2 42
14.5%
1 29
 
10.0%
3 23
 
7.9%
4 15
 
5.2%
6 13
 
4.5%
5 10
 
3.4%
7 6
 
2.1%
9 5
 
1.7%

V23
Categorical

Distinct3
Distinct (%)1.0%
Missing1
Missing (%)0.3%
Memory size2.5 KiB
1
178 
2
77 
3
44 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters299
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 178
59.3%
2 77
25.7%
3 44
 
14.7%
(Missing) 1
 
0.3%

Length

2025-07-01T22:46:11.170671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:11.228957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 178
59.5%
2 77
25.8%
3 44
 
14.7%

Most occurring characters

ValueCountFrequency (%)
1 178
59.5%
2 77
25.8%
3 44
 
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 178
59.5%
2 77
25.8%
3 44
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 178
59.5%
2 77
25.8%
3 44
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 178
59.5%
2 77
25.8%
3 44
 
14.7%

V24
Categorical

High correlation 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
1
191 
2
109 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 191
63.7%
2 109
36.3%

Length

2025-07-01T22:46:11.295700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:11.354426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 191
63.7%
2 109
36.3%

Most occurring characters

ValueCountFrequency (%)
1 191
63.7%
2 109
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 191
63.7%
2 109
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 191
63.7%
2 109
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 191
63.7%
2 109
36.3%

V25
Real number (ℝ)

Zeros 

Distinct61
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3657.88
Minimum0
Maximum41110
Zeros56
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-01T22:46:11.603294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12111.75
median2673.5
Q33209
95-th percentile9400
Maximum41110
Range41110
Interquartile range (IQR)1097.25

Descriptive statistics

Standard deviation5399.5135
Coefficient of variation (CV)1.4761319
Kurtosis21.346565
Mean3657.88
Median Absolute Deviation (MAD)561.5
Skewness4.3457339
Sum1097364
Variance29154746
MonotonicityNot monotonic
2025-07-01T22:46:11.694725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
18.7%
3111 33
 
11.0%
3205 29
 
9.7%
2208 20
 
6.7%
2205 13
 
4.3%
2209 11
 
3.7%
4205 11
 
3.7%
2124 9
 
3.0%
1400 8
 
2.7%
31110 7
 
2.3%
Other values (51) 103
34.3%
ValueCountFrequency (%)
0 56
18.7%
300 1
 
0.3%
400 5
 
1.7%
1111 1
 
0.3%
1124 1
 
0.3%
1400 8
 
2.7%
2111 3
 
1.0%
2112 5
 
1.7%
2113 6
 
2.0%
2124 9
 
3.0%
ValueCountFrequency (%)
41110 1
 
0.3%
31110 7
2.3%
21110 1
 
0.3%
12208 1
 
0.3%
11400 1
 
0.3%
11300 1
 
0.3%
11124 2
 
0.7%
9400 2
 
0.7%
9000 1
 
0.3%
8400 2
 
0.7%

V26
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.226667
Minimum0
Maximum7111
Zeros293
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-01T22:46:11.771163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7111
Range7111
Interquartile range (IQR)0

Descriptive statistics

Standard deviation649.56923
Coefficient of variation (CV)7.1993043
Kurtosis75.181945
Mean90.226667
Median Absolute Deviation (MAD)0
Skewness8.3134244
Sum27068
Variance421940.19
MonotonicityNot monotonic
2025-07-01T22:46:11.836838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 293
97.7%
3111 3
 
1.0%
1400 1
 
0.3%
7111 1
 
0.3%
6112 1
 
0.3%
3112 1
 
0.3%
ValueCountFrequency (%)
0 293
97.7%
1400 1
 
0.3%
3111 3
 
1.0%
3112 1
 
0.3%
6112 1
 
0.3%
7111 1
 
0.3%
ValueCountFrequency (%)
7111 1
 
0.3%
6112 1
 
0.3%
3112 1
 
0.3%
3111 3
 
1.0%
1400 1
 
0.3%
0 293
97.7%

V27
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
299 
2209
 
1

Length

Max length4
Median length1
Mean length1.01
Min length1

Characters and Unicode

Total characters303
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 299
99.7%
2209 1
 
0.3%

Length

2025-07-01T22:46:11.928276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:11.980522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 299
99.7%
2209 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 300
99.0%
2 2
 
0.7%
9 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 303
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 300
99.0%
2 2
 
0.7%
9 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 303
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 300
99.0%
2 2
 
0.7%
9 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 303
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 300
99.0%
2 2
 
0.7%
9 1
 
0.3%

V28
Categorical

High correlation 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2
201 
1
99 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 201
67.0%
1 99
33.0%

Length

2025-07-01T22:46:12.036771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T22:46:12.090088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 201
67.0%
1 99
33.0%

Most occurring characters

ValueCountFrequency (%)
2 201
67.0%
1 99
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 201
67.0%
1 99
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 201
67.0%
1 99
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 201
67.0%
1 99
33.0%

Interactions

2025-07-01T22:46:05.535006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:53.912966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:55.197821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:57.473310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:59.470591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:01.876496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:04.464304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:05.603795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:53.981069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:55.423687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:57.639808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:59.712181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:02.294930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:04.529892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:05.969925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:54.363307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:55.782912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:57.982701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:00.254376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:02.661952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:04.735709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:06.140926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:54.536326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:56.252841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:58.397926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:00.578753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:02.986924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:04.896119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:06.387329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:54.796475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:56.639683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:58.756180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:00.978014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:03.395674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:05.147120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:06.644947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:55.044408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:57.039626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:59.129622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:01.403229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:03.953454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:05.406878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:06.719956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:55.111620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:57.252681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:45:59.285640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:01.629270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:04.212824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T22:46:05.461705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-01T22:46:12.162380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
V1V10V11V12V13V14V15V16V17V18V19V2V20V21V22V23V24V25V26V27V28V3V4V5V6V7V8V9
V11.0000.0470.3240.2900.2650.1120.1030.1120.2330.4680.0000.0500.0000.2070.3750.1720.5960.0290.0710.0000.0000.1000.2380.1710.1070.1590.2400.192
V100.0471.0000.2560.2480.2690.0760.2080.0000.2140.1950.2040.0000.0000.3700.0680.2360.1680.0100.0001.0000.0000.0000.0000.3010.0000.2240.3070.377
V110.3240.2561.0000.2780.2710.0680.1400.1160.2120.2770.2290.0000.1380.2270.2030.3590.4550.0780.0001.0000.1270.0000.1490.2830.1850.2190.2850.270
V120.2900.2480.2781.0000.2820.0000.1990.3600.2650.2590.2950.0000.0610.1990.3010.2310.3730.0000.0000.1000.0510.0000.0000.1340.0000.2590.3100.330
V130.2650.2690.2710.2821.0000.1630.1200.0000.2730.3500.2170.0000.0000.1150.2300.2740.3730.0600.0591.0000.1590.0580.1100.2720.1910.2270.2870.221
V140.1120.0760.0680.0000.1631.0000.0000.0000.1780.0000.1950.1660.0590.0000.2860.0000.0280.0000.0000.0000.0000.1270.2450.1590.0000.0590.1410.096
V150.1030.2080.1400.1990.1200.0001.0000.0000.1510.2310.2520.0040.1650.1560.2340.1650.2120.0840.0001.0000.0000.0000.0000.3710.1640.1510.1690.189
V160.1120.0000.1160.3600.0000.0000.0001.0000.4570.2840.0000.5060.2930.0800.8160.2310.0690.0001.0001.0000.5340.4820.0000.2730.1990.0000.0000.127
V170.2330.2140.2120.2650.2730.1780.1510.4571.0000.3230.1480.1550.0000.2280.3440.1820.2570.0610.0000.0000.0000.1910.0000.3310.1240.1820.2040.193
V180.4680.1950.2770.2590.3500.0000.2310.2840.3231.0000.1680.0340.0960.1410.1990.2270.5740.0800.0001.0000.2010.0000.0000.1150.1210.1080.2460.073
V190.0000.2040.2290.2950.2170.1950.2520.0000.1480.1681.0000.0000.1560.2600.0000.4090.2410.080-0.0450.0000.409-0.1470.0620.4730.0680.2580.2480.309
V20.0500.0000.0000.0000.0000.1660.0040.5060.1550.0340.0001.0000.2490.0000.4590.1220.0000.1830.0000.0680.0350.6760.1130.6250.4160.0000.0000.182
V200.0000.0000.1380.0610.0000.0590.1650.2930.0000.0960.1560.2491.0000.2250.0520.3210.000-0.1840.2050.4320.3280.3440.005-0.0680.0000.0690.0000.225
V210.2070.3700.2270.1990.1150.0000.1560.0800.2280.1410.2600.0000.2251.0000.2790.2560.3540.0000.0000.0470.0370.1100.2650.0000.2000.2950.3110.324
V220.3750.0680.2030.3010.2300.2860.2340.8160.3440.1990.0000.4590.0520.2791.0000.3860.3780.4350.0000.0000.7630.5770.0000.3500.0000.3110.4510.136
V230.1720.2360.3590.2310.2740.0000.1650.2310.1820.2270.4090.1220.3210.2560.3861.0000.3250.1230.0570.0000.1240.1310.3350.2900.1680.2730.3220.302
V240.5960.1680.4550.3730.3730.0280.2120.0690.2570.5740.2410.0000.0000.3540.3780.3251.0000.1030.0630.0000.0000.0700.2510.3130.2510.2470.3320.280
V250.0290.0100.0780.0000.0600.0000.0840.0000.0610.0800.0800.183-0.1840.0000.4350.1230.1031.0000.1080.0000.131-0.031-0.0080.2410.3820.0000.0690.000
V260.0710.0000.0000.0000.0590.0000.0001.0000.0000.000-0.0450.0000.2050.0000.0000.0570.0630.1081.0000.4850.0000.115-0.0040.0350.0000.0350.0000.000
V270.0001.0001.0000.1001.0000.0001.0001.0000.0001.0000.0000.0680.4320.0470.0000.0000.0000.0000.4851.0000.0000.0000.0000.0000.5811.0001.0001.000
V280.0000.0000.1270.0510.1590.0000.0000.5340.0000.2010.4090.0350.3280.0370.7630.1240.0000.1310.0000.0001.0000.0650.4640.2960.1690.1450.0000.078
V30.1000.0000.0000.0000.0580.1270.0000.4820.1910.000-0.1470.6760.3440.1100.5770.1310.070-0.0310.1150.0000.0651.0000.1610.1640.2470.0000.0000.207
V40.2380.0000.1490.0000.1100.2450.0000.0000.0000.0000.0620.1130.0050.2650.0000.3350.251-0.008-0.0040.0000.4640.1611.0000.2360.2800.1760.4520.197
V50.1710.3010.2830.1340.2720.1590.3710.2730.3310.1150.4730.625-0.0680.0000.3500.2900.3130.2410.0350.0000.2960.1640.2361.0000.2150.2240.2850.256
V60.1070.0000.1850.0000.1910.0000.1640.1990.1240.1210.0680.4160.0000.2000.0000.1680.2510.3820.0000.5810.1690.2470.2800.2151.0000.2280.2650.261
V70.1590.2240.2190.2590.2270.0590.1510.0000.1820.1080.2580.0000.0690.2950.3110.2730.2470.0000.0351.0000.1450.0000.1760.2240.2281.0000.3190.241
V80.2400.3070.2850.3100.2870.1410.1690.0000.2040.2460.2480.0000.0000.3110.4510.3220.3320.0690.0001.0000.0000.0000.4520.2850.2650.3191.0000.287
V90.1920.3770.2700.3300.2210.0960.1890.1270.1930.0730.3090.1820.2250.3240.1360.3020.2800.0000.0001.0000.0780.2070.1970.2560.2610.2410.2871.000

Missing values

2025-07-01T22:46:06.895247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-01T22:46:07.071597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-01T22:46:07.410044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

V1V2V3V4V5V6V7V8V9V10V11V12V13V14V15V16V17V18V19V20V21V22V23V24V25V26V27V28
02153010138.50662833NaN2544NaNNaNNaN3545.008.40NaNNaN2211300002
11153481739.28820NaNNaN41342NaNNaNNaN42508522322208002
22153033438.3040241131331NaNNaNNaN1133.006.70NaNNaN120001
319529040939.10164844162244125.003NaN48.007.2035.30212208001
42153025537.3010435NaNNaN62NaNNaNNaNNaNNaNNaNNaNNaN74.007.40NaNNaN224300002
521528355NaNNaNNaN213123221NaN33NaNNaNNaNNaN120002
61152680237.904816111133311NaN3537.007.00NaNNaN113124002
711529607NaN60NaN3NaNNaN1NaN4221NaN3444.008.30NaNNaN212208002
821530051NaN8036343144421NaN3538.006.20NaNNaN313205002
929529962938.3090NaN1NaN1153121NaN3NaN40.006.2012.20120001
V1V2V3V4V5V6V7V8V9V10V11V12V13V14V15V16V17V18V19V20V21V22V23V24V25V26V27V28
2902153505438.64516212111NaNNaNNaNNaN114358NaNNaN120002
2911152889038.9080443331233227.003154.006.503NaN217111002
2921153003437.00662013214331NaNNaN1535.006.902NaN2131110002
29311534004NaN78243331NaN3NaN21NaNNaN44362NaN2322209002
2942153390238.540161111211NaNNaNNaN323767NaNNaN120002
29511533886NaN120704NaN4224NaNNaNNaNNaNNaN55565NaNNaN323205002
2962152770237.207224324243331NaN4444.00NaN33.30312208001
2971152938637.507230434144321NaN3560.006.80NaNNaN213205002
2981153061236.5010024333133331NaN4450.006.0033.40112208001
2991153461837.24020NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN41366211326112002